Restructured STT and enabled prosody tags for generative Polly

examples/foundational/07m-interruptible-aws.py

@@ -5,7 +5,6 @@
 #
 
 import argparse
-import os
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -14,13 +13,13 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.aws import PollyTTSService, TranscribeSTTService
-from pipecat.services.openai import OpenAILLMService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.services.aws.llm import BedrockLLMService, BedrockLLMContext
+from pipecat.services.aws.stt import TranscribeSTTService
+from pipecat.services.aws.tts import PollyTTSService
 
 load_dotenv(override=True)
 
@@ -43,20 +42,30 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         region="us-west-2",  # only specific regions support generative TTS
         voice_id="Joanna",
         params=PollyTTSService.InputParams(
-            engine="generative", language=Language.EN_GB, rate="1.05"
+            engine="generative",
+            language=Language.EN_US,
+            rate="1.1"
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = BedrockLLMService(
+        aws_region="us-west-2",
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=BedrockLLMService.InputParams(
+            temperature=0.8,
+            latency="optimized"
+        )
+    )
 
     messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
+            {
+                "role": "system",
+                "content": [{"text": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way."}],
+            },
+        ]
+    )
 
-    context = OpenAILLMContext(messages)
+    context = BedrockLLMContext(messages)
     context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
@@ -68,8 +77,8 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
             tts,  # TTS
             transport.output(),  # Transport bot output
             context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
+         ]
+     )
 
     task = PipelineTask(
         pipeline,
@@ -85,16 +94,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        messages.append({"role": "user", "content": [{"text": "Please introduce yourself to the user."}]})
         await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-
-    @transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=False)